
// g++-4.4 bench_gemm.cpp -I .. -O2 -DNDEBUG -lrt -fopenmp && OMP_NUM_THREADS=2  ./a.out
// icpc bench_gemm.cpp -I .. -O3 -DNDEBUG -lrt -openmp  && OMP_NUM_THREADS=2  ./a.out

// Compilation options:
//
// -DSCALAR=std::complex<double>
// -DSCALARA=double or -DSCALARB=double
// -DHAVE_BLAS
// -DDECOUPLED
//

#include <Eigen/Core>
#include <bench/BenchTimer.h>
#include <iostream>

using namespace std;
using namespace Eigen;

#ifndef SCALAR
// #define SCALAR std::complex<float>
#    define SCALAR float
#endif

#ifndef SCALARA
#    define SCALARA SCALAR
#endif

#ifndef SCALARB
#    define SCALARB SCALAR
#endif

typedef SCALAR                               Scalar;
typedef NumTraits<Scalar>::Real              RealScalar;
typedef Matrix<SCALARA, Dynamic, Dynamic>    A;
typedef Matrix<SCALARB, Dynamic, Dynamic>    B;
typedef Matrix<Scalar, Dynamic, Dynamic>     C;
typedef Matrix<RealScalar, Dynamic, Dynamic> M;

#ifdef HAVE_BLAS

extern "C" {
#    include <Eigen/src/misc/blas.h>
}

static float                fone    = 1;
static float                fzero   = 0;
static double               done    = 1;
static double               szero   = 0;
static std::complex<float>  cfone   = 1;
static std::complex<float>  cfzero  = 0;
static std::complex<double> cdone   = 1;
static std::complex<double> cdzero  = 0;
static char                 notrans = 'N';
static char                 trans   = 'T';
static char                 nonunit = 'N';
static char                 lower   = 'L';
static char                 right   = 'R';
static int                  intone  = 1;

void blas_gemm(const MatrixXf& a, const MatrixXf& b, MatrixXf& c)
{
    int M   = c.rows();
    int N   = c.cols();
    int K   = a.cols();
    int lda = a.rows();
    int ldb = b.rows();
    int ldc = c.rows();

    sgemm_(&notrans, &notrans, &M, &N, &K, &fone, const_cast<float*>(a.data()), &lda, const_cast<float*>(b.data()), &ldb, &fone, c.data(), &ldc);
}

EIGEN_DONT_INLINE void blas_gemm(const MatrixXd& a, const MatrixXd& b, MatrixXd& c)
{
    int M   = c.rows();
    int N   = c.cols();
    int K   = a.cols();
    int lda = a.rows();
    int ldb = b.rows();
    int ldc = c.rows();

    dgemm_(&notrans, &notrans, &M, &N, &K, &done, const_cast<double*>(a.data()), &lda, const_cast<double*>(b.data()), &ldb, &done, c.data(), &ldc);
}

void blas_gemm(const MatrixXcf& a, const MatrixXcf& b, MatrixXcf& c)
{
    int M   = c.rows();
    int N   = c.cols();
    int K   = a.cols();
    int lda = a.rows();
    int ldb = b.rows();
    int ldc = c.rows();

    cgemm_(&notrans, &notrans, &M, &N, &K, (float*)&cfone, const_cast<float*>((const float*)a.data()), &lda, const_cast<float*>((const float*)b.data()), &ldb, (float*)&cfone, (float*)c.data(), &ldc);
}

void blas_gemm(const MatrixXcd& a, const MatrixXcd& b, MatrixXcd& c)
{
    int M   = c.rows();
    int N   = c.cols();
    int K   = a.cols();
    int lda = a.rows();
    int ldb = b.rows();
    int ldc = c.rows();

    zgemm_(&notrans, &notrans, &M, &N, &K, (double*)&cdone, const_cast<double*>((const double*)a.data()), &lda, const_cast<double*>((const double*)b.data()), &ldb, (double*)&cdone, (double*)c.data(), &ldc);
}



#endif

void matlab_cplx_cplx(const M& ar, const M& ai, const M& br, const M& bi, M& cr, M& ci)
{
    cr.noalias() += ar * br;
    cr.noalias() -= ai * bi;
    ci.noalias() += ar * bi;
    ci.noalias() += ai * br;
    // [cr ci] += [ar ai] * br + [-ai ar] * bi
}

void matlab_real_cplx(const M& a, const M& br, const M& bi, M& cr, M& ci)
{
    cr.noalias() += a * br;
    ci.noalias() += a * bi;
}

void matlab_cplx_real(const M& ar, const M& ai, const M& b, M& cr, M& ci)
{
    cr.noalias() += ar * b;
    ci.noalias() += ai * b;
}

template<typename A, typename B, typename C>
EIGEN_DONT_INLINE void gemm(const A& a, const B& b, C& c)
{
    c.noalias() += a * b;
}

int main(int argc, char** argv)
{
    std::ptrdiff_t l1 = internal::queryL1CacheSize();
    std::ptrdiff_t l2 = internal::queryTopLevelCacheSize();
    std::cout << "L1 cache size     = " << (l1 > 0 ? l1 / 1024 : -1) << " KB\n";
    std::cout << "L2/L3 cache size  = " << (l2 > 0 ? l2 / 1024 : -1) << " KB\n";
    typedef internal::gebp_traits<Scalar, Scalar> Traits;
    std::cout << "Register blocking = " << Traits::mr << " x " << Traits::nr << "\n";

    int rep   = 1;   // number of repetitions per try
    int tries = 2;   // number of tries, we keep the best

    int s           = 2048;
    int m           = s;
    int n           = s;
    int p           = s;
    int cache_size1 = -1, cache_size2 = l2, cache_size3 = 0;

    bool need_help = false;
    for ( int i = 1; i < argc; ) {
        if ( argv[i][0] == '-' ) {
            if ( argv[i][1] == 's' ) {
                ++i;
                s = atoi(argv[i++]);
                m = n = p = s;
                if ( argv[i][0] != '-' ) {
                    n = atoi(argv[i++]);
                    p = atoi(argv[i++]);
                }
            }
            else if ( argv[i][1] == 'c' ) {
                ++i;
                cache_size1 = atoi(argv[i++]);
                if ( argv[i][0] != '-' ) {
                    cache_size2 = atoi(argv[i++]);
                    if ( argv[i][0] != '-' )
                        cache_size3 = atoi(argv[i++]);
                }
            }
            else if ( argv[i][1] == 't' ) {
                ++i;
                tries = atoi(argv[i++]);
            }
            else if ( argv[i][1] == 'p' ) {
                ++i;
                rep = atoi(argv[i++]);
            }
        }
        else {
            need_help = true;
            break;
        }
    }

    if ( need_help ) {
        std::cout << argv[0] << " -s <matrix sizes> -c <cache sizes> -t <nb tries> -p <nb repeats>\n";
        std::cout << "   <matrix sizes> : size\n";
        std::cout << "   <matrix sizes> : rows columns depth\n";
        return 1;
    }

#if EIGEN_VERSION_AT_LEAST(3, 2, 90)
    if ( cache_size1 > 0 )
        setCpuCacheSizes(cache_size1, cache_size2, cache_size3);
#endif

    A a(m, p);
    a.setRandom();
    B b(p, n);
    b.setRandom();
    C c(m, n);
    c.setOnes();
    C rc = c;

    std::cout << "Matrix sizes = " << m << "x" << p << " * " << p << "x" << n << "\n";
    std::ptrdiff_t mc(m), nc(n), kc(p);
    internal::computeProductBlockingSizes<Scalar, Scalar>(kc, mc, nc);
    std::cout << "blocking size (mc x kc) = " << mc << " x " << kc << "\n";

    C r = c;

// check the parallel product is correct
#if defined EIGEN_HAS_OPENMP
    Eigen::initParallel();
    int procs = omp_get_max_threads();
    if ( procs > 1 ) {
#    ifdef HAVE_BLAS
        blas_gemm(a, b, r);
#    else
        omp_set_num_threads(1);
        r.noalias() += a * b;
        omp_set_num_threads(procs);
#    endif
        c.noalias() += a * b;
        if ( !r.isApprox(c) ) std::cerr << "Warning, your parallel product is crap!\n\n";
    }
#elif defined HAVE_BLAS
    blas_gemm(a, b, r);
    c.noalias() += a * b;
    if ( !r.isApprox(c) ) {
        std::cout << (r - c).norm() << "\n";
        std::cerr << "Warning, your product is crap!\n\n";
    }
#else
    if ( 1. * m * n * p < 2000. * 2000 * 2000 ) {
        gemm(a, b, c);
        r.noalias() += a.cast<Scalar>().lazyProduct(b.cast<Scalar>());
        if ( !r.isApprox(c) ) {
            std::cout << (r - c).norm() << "\n";
            std::cerr << "Warning, your product is crap!\n\n";
        }
    }
#endif

#ifdef HAVE_BLAS
    BenchTimer tblas;
    c = rc;
    BENCH(tblas, tries, rep, blas_gemm(a, b, c));
    std::cout << "blas  cpu         " << tblas.best(CPU_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / tblas.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << tblas.total(CPU_TIMER) << "s)\n";
    std::cout << "blas  real        " << tblas.best(REAL_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / tblas.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t(" << tblas.total(REAL_TIMER) << "s)\n";
#endif

    BenchTimer tmt;
    c = rc;
    BENCH(tmt, tries, rep, gemm(a, b, c));
    std::cout << "eigen3 cpu         " << tmt.best(CPU_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / tmt.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << tmt.total(CPU_TIMER) << "s)\n";
    std::cout << "eigen3 real        " << tmt.best(REAL_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / tmt.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t(" << tmt.total(REAL_TIMER) << "s)\n";

#ifdef EIGEN_HAS_OPENMP
    if ( procs > 1 ) {
        BenchTimer tmono;
        omp_set_num_threads(1);
        Eigen::setNbThreads(1);
        c = rc;
        BENCH(tmono, tries, rep, gemm(a, b, c));
        std::cout << "eigen mono cpu    " << tmono.best(CPU_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / tmono.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << tmono.total(CPU_TIMER) << "s)\n";
        std::cout << "eigen mono real   " << tmono.best(REAL_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / tmono.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t(" << tmono.total(REAL_TIMER) << "s)\n";
        std::cout << "mt speed up x" << tmono.best(CPU_TIMER) / tmt.best(REAL_TIMER) << " => " << (100.0 * tmono.best(CPU_TIMER) / tmt.best(REAL_TIMER)) / procs << "%\n";
    }
#endif

    if ( 1. * m * n * p < 30 * 30 * 30 ) {
        BenchTimer tmt;
        c = rc;
        BENCH(tmt, tries, rep, c.noalias() += a.lazyProduct(b));
        std::cout << "lazy cpu         " << tmt.best(CPU_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / tmt.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << tmt.total(CPU_TIMER) << "s)\n";
        std::cout << "lazy real        " << tmt.best(REAL_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / tmt.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t(" << tmt.total(REAL_TIMER) << "s)\n";
    }

#ifdef DECOUPLED
    if ( (NumTraits<A::Scalar>::IsComplex) && (NumTraits<B::Scalar>::IsComplex) ) {
        M ar(m, p);
        ar.setRandom();
        M ai(m, p);
        ai.setRandom();
        M br(p, n);
        br.setRandom();
        M bi(p, n);
        bi.setRandom();
        M cr(m, n);
        cr.setRandom();
        M ci(m, n);
        ci.setRandom();

        BenchTimer t;
        BENCH(t, tries, rep, matlab_cplx_cplx(ar, ai, br, bi, cr, ci));
        std::cout << "\"matlab\" cpu    " << t.best(CPU_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / t.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(CPU_TIMER) << "s)\n";
        std::cout << "\"matlab\" real   " << t.best(REAL_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / t.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
    }
    if ( (!NumTraits<A::Scalar>::IsComplex) && (NumTraits<B::Scalar>::IsComplex) ) {
        M a(m, p);
        a.setRandom();
        M br(p, n);
        br.setRandom();
        M bi(p, n);
        bi.setRandom();
        M cr(m, n);
        cr.setRandom();
        M ci(m, n);
        ci.setRandom();

        BenchTimer t;
        BENCH(t, tries, rep, matlab_real_cplx(a, br, bi, cr, ci));
        std::cout << "\"matlab\" cpu    " << t.best(CPU_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / t.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(CPU_TIMER) << "s)\n";
        std::cout << "\"matlab\" real   " << t.best(REAL_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / t.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
    }
    if ( (NumTraits<A::Scalar>::IsComplex) && (!NumTraits<B::Scalar>::IsComplex) ) {
        M ar(m, p);
        ar.setRandom();
        M ai(m, p);
        ai.setRandom();
        M b(p, n);
        b.setRandom();
        M cr(m, n);
        cr.setRandom();
        M ci(m, n);
        ci.setRandom();

        BenchTimer t;
        BENCH(t, tries, rep, matlab_cplx_real(ar, ai, b, cr, ci));
        std::cout << "\"matlab\" cpu    " << t.best(CPU_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / t.best(CPU_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(CPU_TIMER) << "s)\n";
        std::cout << "\"matlab\" real   " << t.best(REAL_TIMER) / rep << "s  \t" << (double(m) * n * p * rep * 2 / t.best(REAL_TIMER)) * 1e-9 << " GFLOPS \t(" << t.total(REAL_TIMER) << "s)\n";
    }
#endif

    return 0;
}
